Gomez I.G.,University of Washington |
Tang J.,University of Washington |
Wilson C.L.,University of Washington |
Yan W.,University of Washington |
And 4 more authors.
Journal of Biological Chemistry | Year: 2012
Macrophage exiting from inflammatory sites is critical to limit the local innate immune response. With tissue insult, resident tissue macrophages rapidly efflux to lymph nodes where they modulate the adaptive immune response, and inflammatory macrophages attracted to the site of injury then exit during the resolution phase. However, the mechanisms that regulate macrophage efflux are poorly understood. This study has investigated soluble forms of integrin β2 whose levels are elevated in experimental peritonitis at times when macrophages are exiting the peritoneum, suggesting that its proteolytic shedding may be involved in macrophage efflux. Both constitutive and inducible metalloproteinase-dependent shedding of integrin β2 from mouse macrophages are demonstrated. Soluble integrin β2 is primarily released as a heterodimeric complex with αM that retains its ability to bind its ligands intracellular adhesion molecule-1, fibrin, and collagen and thus may serve as a soluble antagonist. In a model of accelerated exiting, administration of a metalloproteinase inhibitor prevents macrophage efflux by 50% and impedes loss of macrophage integrin β2 from the cell surface. Exiting of peritoneal macrophages in mice lacking integrin β2 is accelerated, and antibody disruption of integrin β2-substrate interactions can reverse 50% of the metalloprotease inhibitor blockade of macrophage exiting. Thus, our study demonstrates the ability of metalloproteinase-mediated shedding of integrin β2 to promote macrophage efflux from inflammatory sites, and the release of soluble integrin heterodimers may also limit local inflammation. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc.
Downey M.,University of Ottawa |
Baetz K.,Institute of Systems Biology
Briefings in Functional Genomics | Year: 2016
Acetylation is a dynamic post-translational modification that is attached to protein substrates by lysine acetyltransferases (KATs) and removed by lysine deacetylases (KDACs). While these enzymes are best characterized as histone modifiers and regulators of gene transcription, work in a number of systems highlights that acetylation is a pervasive modification and suggests a broad scope for KAT and KDAC functions in the cell. As we move beyond generating lists of acetylated proteins, the acetylation field is in dire need of robust tools to connect acetylation and deacetylation machineries to their respective substrates and to dissect the function of individual sites. The Saccharomyces cerevisiae model system provides such a toolkit in the context of both tried and true genetic techniques and cutting-edge proteomic and cell imaging methods. Here, we review these methods in the context of their contributions to acetylation research thus far and suggest strategies for addressing lingering questions in the field. © The Author 2015.
Ng S.B.,University of Washington |
Buckingham K.J.,University of Washington |
Lee C.,University of Washington |
Bigham A.W.,University of Washington |
And 10 more authors.
Nature Genetics | Year: 2010
We demonstrate the first successful application of exome sequencing to discover the gene for a rare mendelian disorder of unknown cause, Miller syndrome (MIM%263750). For four affected individuals in three independent kindreds, we captured and sequenced coding regions to a mean coverage of 40× and sufficient depth to call variants at ∼97% of each targeted exome. Filtering against public SNP databases and eight HapMap exomes for genes with two previously unknown variants in each of the four individuals identified a single candidate gene, DHODH, which encodes a key enzyme in the pyrimidine de novo biosynthesis pathway. Sanger sequencing confirmed the presence of DHODH mutations in three additional families with Miller syndrome. Exome sequencing of a small number of unrelated affected individuals is a powerful, efficient strategy for identifying the genes underlying rare mendelian disorders and will likely transform the genetic analysis of monogenic traits. © 2010 Nature America, Inc. All rights reserved.
DeGracia D.J.,Wayne State University |
Huang Z.-F.,Wayne State University |
Huang S.,Institute of Systems Biology
Journal of Cerebral Blood Flow and Metabolism | Year: 2012
Multifactorial injuries, such as ischemia, trauma, etc., have proven stubbornly elusive to clinical therapeutics, in spite of the binary outcome of recovery or death. This may be due, in part, to the lack of formal approaches to cell injury. We present a minimal system of nonlinear ordinary differential equations describing a theory of cell injury dynamics. A mutual antagonism between injury-driven total damage and total induced stress responses gives rise to attractors representing recovery or death. Solving across a range of injury magnitudes defines an 'injury course' containing a well-defined tipping point between recovery and death. Via the model, therapeutics is the diverting of a system on a pro-death trajectory to a pro-survival trajectory on bistable phase planes. The model plausibly explains why laboratory-based therapies have tended to fail clinically. A survival outcome is easy to achieve when lethal injury is close to the tipping point, but becomes progressively difficult as injury magnitudes increase, and there is an upper limit to salvageable injuries. The model offers novel insights into cell injury that may assist in overcoming barriers that have prevented development of clinically effective therapies for multifactorial conditions, as exemplified by brain ischemia. © 2012 ISCBFM All rights reserved.
Sobolev B.,Russian Academy of Medical Sciences |
Filimonov D.,Russian Academy of Medical Sciences |
Lagunin A.,Russian Academy of Medical Sciences |
Zakharov A.,Russian Academy of Medical Sciences |
And 3 more authors.
BMC Bioinformatics | Year: 2010
Background: The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.Results: We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH®database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.Conclusions: It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at http://www.ibmc.msk.ru/PAAS/. © 2010 Sobolev et al; licensee BioMed Central Ltd.